Usage and quality for a configuration management database are often affected by the accuracy and timeliness of relationship information cataloged in the configuration management database. In particular, configuration management databases typically house information describing various information technology resources in a network datacenter in addition to relationships and other dependencies between the information technology resources. As such, configuration management databases typically contain equal or greater amounts of relationship information relative to the number of resources inventoried therein. Consequently, obtaining and maintaining information describing relationships between resources in a datacenter can be extremely labor-intensive and error-prone, which typically results in configuration management database repositories lacking trust within the information technology community. For example, many organizations have expressed skepticism that dependencies and other relationships between resources in network datacenters can be suitably mapped without harming or degrading performance within the datacenter.
Due to these and other challenges, many of which relate to modeling dependencies between resources in information technology datacenters, developing techniques to improve trust in configuration management database technology remains an important factor in their successful adoption. However, although some technologies have shown the potential to create dependency mappings, existing systems have fallen short in actually making such dependency mappings commonplace in the information technology industry. For example, passive network scanners have been proposed as a technology that can model resources and dependencies in information technology datacenters, but passive network scanners typically require dedicated and hardwired connections to switch equipment deployed within the datacenter. Therefore, existing efforts to model resources and dependencies with passive network scanners often suffer from drawbacks relating to the expense and difficulty associated with rewiring network equipment to communicate with the passive network scanners (e.g., due to organizational, regulatory, or other reasons). In another example, modeling resources and dependencies in information technology datacenters has been attempted with technology that installs agents on every resource in the datacenter. Despite having the potential to model dependencies among the resources in the datacenter, installing agents on each and every resource can introduce intensive labor requirements and resource expenses, which has interfered with the suitability of mass-adoption for this approach to datacenter modeling.
Further still, other attempts to model dependencies between information technology resources include technologies that periodically probe every resource in the datacenter (e.g., sending the resources specific operating system commands designed to elicit responses about TCP/IP dependencies known to the resources). Although these probing techniques also have the potential to model dependencies between the datacenter resources, probing technologies typically suffer from coverage gaps due to intervals between the times when the polling or probing occurs. In other words, a resource dependency model created with periodic probing technologies would only be valid at a particular point in time (i.e., when the probe occurs), and moreover, would immediately become stale due to the inability to reflect any changes to the dependencies that arise subsequent to a particular probe (i.e., another probe would be required to update the dependency model in view of the subsequent changes). As such, despite many efforts in the information technology industry that focus on the constant search for newer and better techniques to map interdependencies between resources in network datacenters, the efforts that the industry has undertaken to date suffer from various drawbacks that interfere with successful mass adoption for configuration management databases.
Another topic that presents an important concern relating to datacenter management relates to suitably detecting security threats within the datacenter. Although various systems have been developed in the domain of network security and intrusion detection, such as tools built around Network Behavior Analysis (NBA) technology, the existing systems typically warehouse information relating to network flows and other activity within the datacenter for forensic (i.e., after-the-fact) analysis. As such, the techniques used in existing security and intrusion detection systems typically require large data warehouses to maintain archives that describe each and every flow reported in the datacenter. However, although existing systems typically maintain large amounts of information that can permit mining for interesting behavior or activity in the datacenter, the usefulness that these existing systems provide tends to be limited to detecting rogue or malicious activity post-mortem. In other words, because systems for detecting security threats that have been developed to date tend to be geared towards supporting user queries and ad-hoc analysis, existing systems often fall short in their ability to monitor activity within a datacenter to detect security threats or rogue behavior in real-time.
Moreover, yet another issue that often imposes difficulties on datacenter management includes assessing the scope and impact that may result from a considered set of proposed changes to information technology resources in the datacenter (e.g., servers, applications, hardware, etc.). The processes typically used to allow participants to understand the full scope of a set of changes can often be difficult, time-consuming, and error-prone, especially in large datacenters that have large numbers of information technology resources. Furthermore, in most (if not all) cases, no single person within an information technology organization knows every function and purpose that the information technology resources serve in the datacenter. Although configuration management databases have been used in various existing systems to support browsing and navigating relationships between information technology resources, the existing systems tend to employ ad-hoc techniques that fall short in their ability to quickly and accurately assess the scope, impact, and potential conflicts that the proposed changes may have in the datacenter. Thus, because efficiency and agility are among the most important concerns to managing modern information technology datacenters, existing systems suffer from various drawbacks in automating datacenter changes, including collaborative decision support among human participants to resolve potential problems with proposed changes.